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A random shuffle method to expand a narrow dataset: A heart failure cohort example

Vascular Pharmacology 2022年 146卷 107041-107041页

作者： Fassina, Lorenzo Muzio, Francesco Paolo Lo Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Surgery Dentistry Paediatrics and Gynaecology University of Verona Verona Italy Department of Medicine and Surgery University of Parma Parma Italy

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Counting Monkeypox Lesions in Patient Photographs: Limits of Agreement of Manual Counts and Artificial Intelligence

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Journal of Investigative Dermatology 2023年第2期143卷 347-350.e4页

作者： McNeil, Andrew J. House, David W. Mbala-Kingebeni, Placide Mbaya, Olivier Tshiani Dodd, Lori E. Cowen, Edward W. Nussenblatt, Véronique Bonnett, Tyler Chen, Ziche Saknite, Inga Dawant, Benoit M. Tkaczyk, Eric R. Dermatology Service and Research Service Department of Veterans Affairs Tennessee Valley Healthcare System Nashville TN United States Department of Dermatology Vanderbilt University Medical Center Nashville TN United States Department of Electrical and Computer Engineering School of Engineering Vanderbilt University Nashville TN United States Institut National de Recherche Biomédicale Kinshasa Democratic Republic Congo Clinical Monitoring Research Program Directorate Frederick National Laboratory for Cancer Research Frederick MD United States Clinical Trials Research Section Division of Clinical Research National Institute of Allergy and Infectious Disease Bethesda MD United States Dermatology Branch National Institute of Arthritis and Musculoskeletal and Skin Diseases Bethesda MD United States Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases Bethesda MD United States Biophotonics Laboratory Institute of Atomic Physics and Spectroscopy University of Latvia Riga Latvia Department of Biomedical Engineering School of Engineering Vanderbilt University Nashville TN United States

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Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)

arXiv

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arXiv 2020年

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Hussain, Sadiq Khosravi, Abbas Koohestani, Afsaneh Zangooei, Mohammad Hossein Abdar, Moloud Beykikhoshk, Adham Shoeibi, Afshin Zare, Assef Panahiazar, Maryam Nahavandi, Saeid Srinivasan, Dipti Atiya, Amir F. Acharya, U. Rajendra Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran System Administrator Dibrugarh University Assam786004 India University of Texas Dallas United States Applied Artificial Intelligence Institute Deakin University Geelong Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Computational Health Sciences University of California San Francisco United States Dept. of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Department of Computer Engineering Faculty of Engineering Cairo University Cairo12613 Egypt Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, application of novel deep learning techniques to deal such uncertainties have significantly increased. Copyright © 2020, The Authors. All rights reserved.

关键词： Monte Carlo methods

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麻醉与意识的脑网络研究进展——框架与临床应用（英文）

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engineering 2023年第1期 77-95页

作者：刘军董康立孙毅 Ioannis Kakkos 黄帆王国正齐鹏陈星张德林 Anastasios Bezerianos 孙煜 Key Laboratory for Biomedical Engineering of Ministry of Education of China Zhejiang University Department of Neurology Sir Run Run Shaw Hospital School of Medicine Zhejiang University School of Electrical and Computer Engineering National Technical University of Athens Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Department of Anesthesiology The First Affiliated Hospital School of Medicine Zhejiang University The N1 Institute for Health Center for Life Sciences National University of Singapore

尽管麻醉与意识之间的关系一直是研究者关注的重点，但目前学界对于麻醉与意识的神经机制理解仍处在初级阶段，极大地限制了麻醉监测和意识评估系统的进一步发展。此外，现有麻醉监测方法难以提供足够的有效信息，对精准麻醉监测的目标...

尽管麻醉与意识之间的关系一直是研究者关注的重点，但目前学界对于麻醉与意识的神经机制理解仍处在初级阶段，极大地限制了麻醉监测和意识评估系统的进一步发展。此外，现有麻醉监测方法难以提供足够的有效信息，对精准麻醉监测的目标构成了障碍。近年来，使用脑网络分析揭示麻醉机制已成为研究热点，其目的是为临床应用提供新的研究思路。针对这一新的研究趋势，本文全面回顾了麻醉相关脑网络研究的最新进展，系统地比较了麻醉和意识的几种潜在的神经机制以及不同层面大脑神经活动的测量方法；从皮层碎片化理论出发，介绍了连通性和网络分析的一些重要的研究方法和相关成果；在总结归纳现有研究成果的基础上，论证了全脑多模态网络数据可以提供现有麻醉监测方法所无法提供的信息；更重要的是，进一步提出简化脑网络分析方法的可行性，这一方法将会在优化现有的临床麻醉监测系统中发挥重要作用。

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Covariance-Free Sparse Bayesian Learning

arXiv

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arXiv 2021年

作者： Lin, Alexander Song, Andrew H. Bilgic, Berkin Ba, Demba The School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States The Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02138 United States Harvard-MIT Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Athinoula A. Martinos Center for Biomedical Imaging CharlestownMA United States Department of Radiology Harvard Medical School BostonMA United States

Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem while also providing uncertainty quantification. The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix. To resolve this issue, we introduce a new method for accelerating SBL inference - named covariance-free expectation maximization (CoFEM) - that avoids explicit computation of the covariance matrix. CoFEM solves multiple linear systems to obtain unbiased estimates of the posterior statistics needed by SBL. This is accomplished by exploiting innovations from numerical linear algebra such as preconditioned conjugate gradient and a little-known diagonal estimation rule. For a large class of compressed sensing matrices, we provide theoretical justifications for why our method scales well in high-dimensional settings. Through simulations, we show that CoFEM can be up to thousands of times faster than existing baselines without sacrificing coding accuracy. Through applications to calcium imaging deconvolution and multi-contrast MRI reconstruction, we show that CoFEM enables SBL to tractably tackle high-dimensional sparse coding problems of practical interest. © 2021, CC BY.

关键词： Maximum principle

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Fundus image quality enhancement for blood vessel detection via a neural network using CLAHE and Wiener filter

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Research on biomedical engineering 2020年第2期36卷 107-119页

作者： dos Santos, Jucelino Cardoso Marciano Carrijo, Gilberto Arantes de Fátima dos Santos Cardoso, Cristiane Ferreira, Júlio César Sousa, Pedro Moises Patrocínio, Ana Cláudia Signal Processing Lab Faculty of Electrical Engineering Federal University of Uberlândia Campus Sta Mônica Av. João Naves de Ávila 2121 Bloco 1N UberlândiaMGCEP 38400-000 Brazil Computer Vision Lab Informatics Nucleo Goiano Federal Institute of Education Science and Technology Campus Urutaí Rodovia Geraldo Silva Nascimento km 2.5 Zona Rural Bloco de Informática UrutaíGOCEP 75790-000 Brazil Biomedical Lab Faculty of Electrical Engineering Federal University of Uberlândia Campus Sta Mônica Av. João Naves de Ávila 2121 Bloco 3N UberlândiaMGCEP 38400-000 Brazil

Purpose: Blood vessel segmentation is the most important step for detecting changes in retinal vascular structures in retinal images. While these images are widely used in clinical diagnosis, they are generally degraded by noise and are limited by low contrast. In this paper, we address the problem of improving fundus image quality for blood vessel detection. Methods: We used contrast limited adaptive histogram equalization (CLAHE) to improve contrast and the Wiener filter for noise reduction. A multilayer artificial neural network was used to optimize the values from CLAHE and the Wiener filter for blood vessel segmentation. Furthermore, several training and classification rounds were performed (3240, with 200 epochs each), using a combination of CLAHE and Wiener parameters and a fixed network configuration. Results: The proposed methodology was tested in the DRIVE database, achieving accuracy, sensitivity, and specificity values of 0.9505, 0.7564, and 0.9696, respectively. Conclusion: The results were encouraging for almost all metrics and comparable to those of state-of-the-art blood vessel segmentation processes. Therefore, the parameter set effectively improved the fundus image quality for blood vessel segmentation, relative to the classification. These results are important since the more precise the segmentation step is, the greater the chances are of building a robust and specialized diagnostic system. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Image quality

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Study of target and non-target interplay in spatial attention task

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Journal of Medical engineering and Technology 2018年第2期42卷 113-120页

作者： Sweeti Joshi, Deepak Panigrahi, B.K. Anand, Sneh Santhosh, Jayasree Centre for Biomedical Engineering IIT Delhi New Delhi India Department of Electrical Engineering IIT Delhi New Delhi India Department of Computer Engineering & Computer Science Manipal International University Negeri Sembilan Malaysia

Selective visual attention is the ability to selectively pay attention to the targets while inhibiting the distractors. This paper aims to study the targets and non-targets interplay in spatial attention task while subject attends to the target object present in one visual hemifield and ignores the distractor present in another visual hemifield. This paper performs the averaged evoked response potential (ERP) analysis and time-frequency analysis. ERP analysis agrees to the left hemisphere superiority over late potentials for the targets present in right visual hemifield. Time-frequency analysis performed suggests two parameters i.e. event-related spectral perturbation (ERSP) and inter-trial coherence (ITC). These parameters show the same properties for the target present in either of the visual hemifields but show the difference while comparing the activity corresponding to the targets and non-targets. In this way, this study helps to visualise the difference between targets present in the left and right visual hemifields and, also the targets and non-targets present in the left and right visual hemifields. These results could be utilised to monitor subjects’ performance in brain–computer interface (BCI) and neurorehabilitation. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Brain computer interface

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Automatic standard plane and diagnostic usability classification in obstetric ultrasounds

WFUMB Ultrasound Open

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WFUMB Ultrasound Open 2024年第2期2卷 100050-100050页

作者： Lim, Adam Abdalla, Mohamed Abolhassani, Farbod Law, Wyanne Fine, Benjamin Sussman, Dafna Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Faculty of Engineering and Architectural Sciences Toronto Ontario Canada Institute for Biomedical Engineering Science and Technology (iBEST) Toronto Metropolitan University and St. Michael's Hospital Toronto Ontario Canada Institute for Better Health Mississauga Ontario Canada Dalla Lana School of Public Health University of Toronto Toronto Ontario Canada Department of Radiology Memorial Sloan Kettering Cancer Center New York NY United States Department of Medical Imaging University of Toronto Toronto Ontario Canada Department of Obstetrics and Gynecology Faculty of Medicine University of Toronto Toronto Ontario Canada

AbstractObjectiveThis study introduces an innovative end-to-end deep learning pipeline designed to automatically classify and order fetal ultrasound standard planes in alignment with the guidelines of the Canadian Association of Radiologists, while also assessing the diagnostic usability of each view. The primary objective is to address the manual and cumbersome challenges that interpreting radiologists encounter in the existing obstetric ultrasound workflow. MethodsWe compiled a diverse dataset, comprising 33,561 de-identified two-dimensional obstetrical ultrasound images acquired from January 1, 2010, to June 1, 2020. This dataset was categorized into 19 distinct classes associated with standard planes and further partitioned into training, validation, and testing subsets via a 60:20:20 stratified split. The standard plane and diagnostic usability networks are founded on a convolutional neural network framework and employ the benefits of transfer learning. ResultsThe standard plane classification network demonstrated promising results by achieving 99.4 % and 98.7 % for accuracy and F1 score, respectively. Subsequently, the diagnostic usability network demonstrated strong performance, registering 80 % accuracy and an 82 % F1 score. Notably, this study is the first to investigate whether deep learning methods can surpass sonographers in the standard plane labeling task, with some instances revealing the algorithm's capacity to rectify sonographer mislabeled planes. ConclusionThe results highlight the algorithm's potential to be integrated into a clinical setting by serving as a reliable assistive tool, alleviating the cognitive workload faced by radiologists and enhancing efficiency and diagnostic outcomes in the current obstetric ultrasound process.

关键词： Deep learning Obstetric ultrasound Convolutional neural network Image classification

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eRAKI: Fast robust artificial neural networks for k-space interpolation (RAKI) with coil combination and joint reconstruction

arXiv

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arXiv 2021年

作者： Yu, Heng Dong, Zijing Arefeen, Yamin Liao, Congyu Setsompop, Kawin Bilgic, Berkin Department of Automation Tsinghua University Beijing China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Athinoula A. Martinos Center for Biomedical Imaging CharlestownMA United States Radiological Sciences Laboratory Stanford University StanfordCA United States Department of Electrical Engineering Stanford University StanfordCA United States Harvard Medical School BostonMA United States Harvard-MIT Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States

RAKI can perform database-free MRI reconstruction by training models using only auto-calibration signal (ACS) from each speciflc scan. As it trains a separate model for each individual coil, learning and inference with RAKI can be computationally prohibitive, particularly for large 3D datasets. In this abstract, we accelerate RAKI by more than 200 times by directly learning a coil-combined target and further improve the reconstruction performance using joint reconstruction across multiple echoes together with an elliptical-CAIPI sampling approach. We further deploy these improvements in quantitative imaging and rapidly obtain T2 and T2* parameter maps from a fast EPTI scan. © 2021, CC0.

关键词： Neural networks

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Relaxation Based Modeling of GMD Induced Cascading Failures in ***

arXiv

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arXiv 2021年

作者： Mate, Adam Barnes, Arthur K. Morley, Steven K. Friz-Trillo, Jacob A. Cotilla-Sanchez, Eduardo Blake, Seán P. The Advanced Network Science Initiative at Los Alamos National Laboratory Los AlamosNM87545 United States The Space Science and Applications Group Los Alamos National Laboratory Los AlamosNM87545 United States The Department of Electrical and Biomedical Engineering The University of Vermont BurlingtonVT05405 United States The School of Electrical Engineering and Computer Science Oregon State University CorvallisOR97331 United States The Heliophysics Science Division NASA Goddard Space Flight Center GreenbeltMD20771 United States

A major risk of geomagnetic disturbances (GMDs) is cascading failure of electrical grids. The modeling of GMD events and cascading outages in power systems is difficult, both independently and jointly, because of the many different mechanisms and physics involved. This paper introduces a relaxation based modeling of GMD-induced cascading failures: the dc approximation-based DCSIMSEP solver was adapted to simulate cascading as a result of GMDs, the full set of ac power flow equations were relaxed to guarantee optimality, and the reactive power losses were modeled while keeping the problem convex. The developed algorithm was implemented in *** - an open-source software specifically designed to model and analyze geomagnetic hazards - and demonstrated to work on the RTS-GMLC-GIC-EAST synthetic test network. © 2021, CC BY.

关键词： Open source software

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